{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td></td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311            生鲜  个   8.0   4.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   6.5   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\cx\\\\文档\\\\实训课\\\\大数据分析综合实训-附件.csv',encoding = 'gbk')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42816 entries, 0 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   顾客编号    42816 non-null  int64  \n",
      " 1   大类编码    42816 non-null  int64  \n",
      " 2   大类名称    42816 non-null  object \n",
      " 3   中类编码    42816 non-null  int64  \n",
      " 4   中类名称    42816 non-null  object \n",
      " 5   小类编码    42816 non-null  int64  \n",
      " 6   小类名称    42816 non-null  object \n",
      " 7   销售日期    42816 non-null  int64  \n",
      " 8   销售月份    42816 non-null  int64  \n",
      " 9   商品编码    42816 non-null  object \n",
      " 10  规格型号    42816 non-null  object \n",
      " 11  商品类型    42816 non-null  object \n",
      " 12  单位      42816 non-null  object \n",
      " 13  销售数量    42814 non-null  float64\n",
      " 14  销售金额    42816 non-null  float64\n",
      " 15  商品单价    42816 non-null  float64\n",
      " 16  是否促销    42816 non-null  object \n",
      "dtypes: float64(3), int64(6), object(8)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#查看数据类型\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42816.000000</td>\n",
       "      <td>42816.000000</td>\n",
       "      <td>42816.000000</td>\n",
       "      <td>42816.000000</td>\n",
       "      <td>4.281600e+04</td>\n",
       "      <td>42816.000000</td>\n",
       "      <td>42814.000000</td>\n",
       "      <td>42816.000000</td>\n",
       "      <td>42816.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>876.944740</td>\n",
       "      <td>17.934884</td>\n",
       "      <td>1799.512495</td>\n",
       "      <td>179959.814976</td>\n",
       "      <td>2.015026e+07</td>\n",
       "      <td>201502.443362</td>\n",
       "      <td>1.199202</td>\n",
       "      <td>10.608517</td>\n",
       "      <td>12.541321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>695.075098</td>\n",
       "      <td>6.061163</td>\n",
       "      <td>608.934950</td>\n",
       "      <td>60896.114008</td>\n",
       "      <td>1.151572e+02</td>\n",
       "      <td>1.149795</td>\n",
       "      <td>2.519092</td>\n",
       "      <td>41.826800</td>\n",
       "      <td>16.658380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>100101.000000</td>\n",
       "      <td>2.015010e+07</td>\n",
       "      <td>201501.000000</td>\n",
       "      <td>-16.000000</td>\n",
       "      <td>-145.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>279.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>120106.000000</td>\n",
       "      <td>2.015013e+07</td>\n",
       "      <td>201501.000000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>3.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>729.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>200103.000000</td>\n",
       "      <td>2.015022e+07</td>\n",
       "      <td>201502.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>6.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1350.250000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2203.000000</td>\n",
       "      <td>220306.000000</td>\n",
       "      <td>2.015040e+07</td>\n",
       "      <td>201504.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.960000</td>\n",
       "      <td>14.992500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2611.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>3436.000000</td>\n",
       "      <td>343699.000000</td>\n",
       "      <td>2.015043e+07</td>\n",
       "      <td>201504.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>5340.000000</td>\n",
       "      <td>890.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               顾客编号          大类编码          中类编码           小类编码          销售日期  \\\n",
       "count  42816.000000  42816.000000  42816.000000   42816.000000  4.281600e+04   \n",
       "mean     876.944740     17.934884   1799.512495  179959.814976  2.015026e+07   \n",
       "std      695.075098      6.061163    608.934950   60896.114008  1.151572e+02   \n",
       "min        0.000000     10.000000   1001.000000  100101.000000  2.015010e+07   \n",
       "25%      279.000000     12.000000   1201.000000  120106.000000  2.015013e+07   \n",
       "50%      729.000000     20.000000   2001.000000  200103.000000  2.015022e+07   \n",
       "75%     1350.250000     22.000000   2203.000000  220306.000000  2.015040e+07   \n",
       "max     2611.000000     34.000000   3436.000000  343699.000000  2.015043e+07   \n",
       "\n",
       "                销售月份          销售数量          销售金额          商品单价  \n",
       "count   42816.000000  42814.000000  42816.000000  42816.000000  \n",
       "mean   201502.443362      1.199202     10.608517     12.541321  \n",
       "std         1.149795      2.519092     41.826800     16.658380  \n",
       "min    201501.000000    -16.000000   -145.000000      0.000000  \n",
       "25%    201501.000000      0.530000      2.900000      3.960000  \n",
       "50%    201502.000000      1.000000      5.700000      6.900000  \n",
       "75%    201504.000000      1.000000     10.960000     14.992500  \n",
       "max    201504.000000    216.000000   5340.000000    890.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() #查看统计性描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号    0\n",
       "大类编码    0\n",
       "大类名称    0\n",
       "中类编码    0\n",
       "中类名称    0\n",
       "小类编码    0\n",
       "小类名称    0\n",
       "销售日期    0\n",
       "销售月份    0\n",
       "商品编码    0\n",
       "规格型号    0\n",
       "商品类型    0\n",
       "单位      0\n",
       "销售数量    2\n",
       "销售金额    0\n",
       "商品单价    0\n",
       "是否促销    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看是否空值\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#填充数据\n",
    "df1=df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9.000000e+00\n",
       "mean     2.281605e+06\n",
       "std      6.701254e+06\n",
       "min      1.199202e+00\n",
       "25%      1.254132e+01\n",
       "50%      8.769447e+02\n",
       "75%      1.799598e+05\n",
       "max      2.015026e+07\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']    #定义使其正常显示中文字体黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEHCAYAAACk6V2yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3df3Ak513n8fdXM6PRzGg0v9ZxYkfx4mITO9je4JOvbHCOXc524pQB43AYJ3BcXagNEBJCgAPOJhdcNvgCMSRUxQFuAdt3WSqkiGNIfCwucMXH2Um0xI6TOLlwF9uJzz6QNBqNpFlpNPreH93TK8kjqRXLO+rZz6tqame6n37m0fTqM62nn+7H3B0RERk8Q/1ugIiIvDQU8CIiA0oBLyIyoBTwIiIDSgEvIjKgFPCSeGb2XjP7uZhlh8ysbGavMbOrzeznzeziF/n+N5nZpeHzW8zsV8LnP2pm1/cob2Y2uqFNhRfTBpFe0v1ugMhOmVkWaLv7arhoCVjeUGbY3ZfN7BeAnwcWAAcuAD4DTAN1YBb4TuCJNds+D3wD6Gx465cBn3L3X9iw/HngT8zsdWE72mY2BLwX+MkeP8Ih4A/N7HXuvgC8CnjMzM5y97aZZd19Kf4nItKbaRy8JI2ZPQC8nCCAX0sQ8KtACvhHwIC6u19lZga8PSxrwO8SBH7XMHCfu//fNfU/BXyL4AthaM3jLOCT3YA3sxzwiXCzVwDPAfvD7aaAi4DPhut/3N2nwu0+CRwHrgZeCeTC7Z4My2aAy9x93ZeWyE4p4CWxzOxngauAR4E54AeBvwLucnc3sxRBMF8BtIEfBqrAf+lWAYwAXyX4QjgZ1vsU8Dp3n+3xnjXgHHd/wsyGgf8NXLZNUx8EDrv7P5vZG4D/TvBl0QauBV4N/Bhwa1j+L8Mje5EXRV00kjhmNgHcAjwL3AS8E1ghCPAPAU+a2X8Fvklw9L5McHT/vcDDwB1rqyM4iv9j4I/CZavAdwF/3+PtLw3f7wfDOr8AvBH4HYKj9rX2Ae8h+AJyM3tF+B5zYXtfDvwH4GcJjugBPkzQhaSAlxdNR/CSKGZ2ALiH4Kj4BqAFlAlCeY7giPxe4Bzg1919LtzuPwM/QPAFsNFHu+XCsr8MXEcQ4F0pgi+DeeBmd//smvJvJehu+dMN9f474Li7fzQsdxXweuDNwJUEXwAnWNP/D1wMvMbdn4/zeYhsZc8FvJmdDXzc3V+/yfqfAW4MX5aBz7r7209X+2RvMLM3Aj8HfGTDqn8P3O3unwzLDQG/RXAkfTPwlQ3lfxf4fnf/5ob6J4APuPv3ha9/GXi5u/9ij7a8BjgcvryK4Mvmb8PXf+fuX9tQ/kucCvhPA3euWf0e4F8p4GU37KkuGjOrAHcDmw4Zc/e7gLvC8r8flpczzyqQJ+jmWKtAcJITM7sA+EPgKeA+4McJjvLXejlBXzhmdi1BV0sLKAI1M5sMy70MSJvZoXDdje7+BTP7XuCDa+o7O3z/88PXP2Vmv+nuf7HJz9EiODG89rXIrthTAU8w0uFGoHv0lSf4c/xlwBPu/o5uQTM7Fzjb3Sd7VSQDL0swSuWnNyx/JUGfOkAT+DN3/7CZ/RlB3/ZTG8qf133i7g8ADwCY2W8DX3L3u8PX7wbK7v6+Ddvngcfc/afCcr8ErLj774WvbwPGtvg59gPv2/BaZFfsqYBf01/aXXSE4JfsfWb2F2Z2ibt/MVz3DsIjeTnzuPtfEnzxb1XmWYKTlhD0n38xfKz1trUvzKxI0M1zGPj1tavofWFgz4sFw4OTGwnGvH9+42qCLyEDvuDuh9Zs9xAwZGZpd1/p/ZOJxLOnAr6H1wDfE/5ZXAbOBb4Y9qseJuhTlTNM2Ifd5IUXInWlgFHgjWHIQ3DE/x6Ck6RrvSpch5llgL8m+BK4qjtsMjTMqb8M1noCmFnzOguk3H3RzF4F/D5w/4ZtsgQXQN0AdMzsf2xY/3GCg5d7N/n5RGLZcydZITiKcfdD4Z/FDXf/EzO7DviGu3/ZzL4P+GF3f3efmyoismft9XvR/BFwrZl9hqCvtTvS4Q0E/akiIrKJPXkELyIiL95eP4IXEZFvkwJeRGRA7ZlRNPv27fP9+/f3uxkiPa2urjI0pOMh2XtOnDgx5e5n9Vq3ZwJ+//79TE7qmiXZm9rtNplMpt/NEHkBM3t6s3U6JBGJYWVF1xxJ8ijgRWJYWNDdeyV5FPAiIgNKAS8SQz6f73cTRHZMAS8Sg06wShIp4EW2cOzYMS666CJGRka46KKLOHbsWL+bJBLbnhkmKbLXHDt2jJtvvpmjR49ywQUX8NWvfpW3vS24u/BNN93U59aJbG/bI3gzK5nZA2Z23Mw+Ec4k36vcUTN7xMxu2WqZSFLcfvvtHD16lMOHD5PP5zl8+DBHjx7l9ttv73fTRGKJ00XzVuBOd78GeJ5gBvl1zOwGgntgXwGcb2YHei3bzYaLvNSefPJJrrzySgBKpRIAV155JU8++WQ/myUS27ZdNO7+4TUvzwL+qUexQ8DHwufHCSYU/u4ey76+diMzO0IwaxPj4+NMTU0BUCgUSKfTNBoNAIaHhykWi0xPTwMwNDREtVql0WjQbrcBKJfLLC0t0Wq1ojpSqRRzc8EUnNlslkKhwMzMzLo6Zmdno4tYKpUKrVaLkyeDeR5GR0cxM5rNJgAjIyPk8/mojlQqRaVSoV6v0+kEc09Uq1UWFxejOorFIu7O/Px8VEcul6NerwOQTqcpl8vMzMywuroa1bGwsMDS0hIAY2NjdDqdaCx2Lpcjm80yOzsLBCcAS6XSujpqtRrNZpPl5WUgCKiVlZWojnw+TyaTiT7jbh3T09O4O2ZGrVZb9xmXSiXa7TaLi4tnxH668MIL+dSnPsWVV17JwsIC5513Hp/+9Kd59atfzdTUlPbTHtlPZ/rv01Zi3y7YzK4AbnP3f91j3VHgQ+7+uJldA1wKHNi4zN3v2Kz+iYkJ160KZC/ZrA/+9ttvVx+87BlmdsLdJ3qti3WS1cyqBFOPvXmTIvNALnw+StD102uZSGJ0Q/yd73wnTz75JBdeeKHCXRJl24APT6r+OfBr7r7ZTW1OEHTBPAocBL4GfKvHMpFEuemmmxToklhxjuDfRtDlcrOZ3Qz8HZBx97UjY+4DHjazc4BrgcsB77FMJJEajUZ0olUkKeKcZL2LYIb3rcrMmdkh4Grg/e7eAOi1TCSJuie1RJJk1y50cvc6p0bNbLpMREROD534FIlB3TOSRAp4kRjURSNJpIAXiaF7MYpIkijgRUQGlAJeJIZCodDvJojsmAJeJIZ0WnfWluRRwIvE0L0BlEiSKOBFRAaUAl4khuHhnvPciOxpCniRGIrFYr+bILJjCniRGLqTLogkiQJeRGRAKeBFYhga0q+KJI/+14rEUK1W+90EkR1TwIvEoHHwkkSxAt7Mzjazh7dY/zNm9lD4eMzM/sDM0mb2zJrlF+9es0VOL91NUpIozpysFeBuYNObcayd9cnMfj8sfwlwzN1/ZXeaKiIiOxHnCL4D3AjMbVfQzM4Fznb3SYI5WK8zs8+Z2VEz0808JLHK5XK/myCyY3HmZJ0DMLM49b2DU/O3fh64yt2fM7N7gDcB968tbGZHgCMA4+PjTE1NAcGd+9LpdNTvOTw8TLFYjMYiDw0NUa1WaTQa0Z/O5XKZpaUlWq1WVEcqlWJuLvheymazFAoFZmZm1tUxOzvLysoKAJVKhVarxcmTJwEYHR3FzGg2mwCMjIyQz+ejOlKpFJVKhXq9TqfTAYKTcYuLi1EdxWIRd2d+fj6qI5fLUa/XgeAmVuVymZmZGVZXV6M6FhYWWFpaAmBsbIxOp8PCwgIAuVyObDbL7OwsAJlMhlKptK6OWq1Gs9lkeXkZCGYkWllZierI5/NkMpnoM+7WMT09jbtjZtRqtXWfcalUot1uR/dGP5P2U7vd5hWveIX20x7fT2fi79NWzN23LBAVNHvI3Q9tsX4I+Hvge9zdzSzr7kvhuncBGXf/wGbbT0xM+OTkZKy2iJxuU1NT7Nu3r9/NEHkBMzvh7hO91u3mKJrXA5/1U98Y95rZQTNLAdcDj+/ie4mIyDZ2HPBm9lozu63HqjcAn1nz+lbgXuAx4BF3f/Dba6JI/2nCD0mi2F00LzV10chetry8rDtKyp50urpoRAZW9+SiSJIo4EVEBpQCXiSGbDbb7yaI7JgCXiQGnWSVJFLAi8TQvRhHJEkU8CIiA0oBLxKDJvyQJNL/WpEYNOGHJJECXiSG7o2oRJJEAS8SQ/cOiSJJooAXERlQCniRGCqVSr+bILJjCniRGLoTX4gkiQJeJIbujEIiSaKAFxEZUAp4kRhGR0f73QSRHVPAi8QQc9J5kT0lVsCb2dlm9vAW69Nm9oyZPRQ+Lg6XHzWzR8zslt1qsEg/NJvNfjdBZMe2DXgzqwB3A1vdL/US4Ji7HwofT5jZDUDK3a8AzjezA7vTZBERiSMdo0wHuBH45BZlLgeuM7PDwBPA24FDwMfC9ceBK4Gvr93IzI4ARwDGx8eZmpoCgntvp9NpGo0GAMPDwxSLRaanp4Hgxk/VapVGo0G73QagXC6ztLQUDWcrFAqkUqloqrVsNkuhUIhu+9qtY3Z2NrpKsVKp0Gq1ohETo6OjmFl09DYyMkI+n4/qSKVSVCoV6vU6nU4HCO5Zsri4GNVRLBZxd+bn56M6crkc9XodgHQ6TblcZmZmhtXV1aiOhYUFlpaWABgbG6PT6bCwsABALpcjm81Gl89nMhlKpdK6Omq1Gs1mk+XlZQBKpRIrKytRHfl8nkwmE33G3Tqmp6dxd8yMWq227jMulUq0220WFxfPuP3U/Ry1n/b2fjoTf5+2EnvSbTN7yN0PbbLuMuBb7v6cmd0DfBz4IeBD7v64mV0DXOrud2xWvybdlr1sdXVVd5SUPel0TLr9RXd/Lnw+CRwA5oFcuGx0F99L5LTThB+SRLsVuvea2UEzSwHXA48DJwi6ZQAOAk/t0nuJiEgMcfrg1zGz1wJvcfe1I2NuBT4KGHC/uz9oZmPAw2Z2DnAtQT+9SCKlUql+N0Fkx2L3wX9blQcjcK4GPuPuz29VVn3wIiI7dzr64Hty97q7f2y7cBfZ67qjNESSRCc+RWLoDtsTSRIFvIjIgFLAi8SgSbcliRTwIjF0rzYUSRIFvEgMmvBDkkgBLyIyoBTwIjEUi8V+N0FkxxTwIjG8lBcEirxUFPAiMXRvTyuSJAp4EZEBpYAXiWFkZKTfTRDZMQW8SAy5XG77QiJ7jAJeJAbdbEySSAEvIjKgFPAiMaTTO54bR6TvYgW8mZ1tZg9vsb5kZg+Y2XEz+4SZDZtZ2syeMbOHwsfFu9dskdNru9nrRfaibQM+nJXpbqCwRbG3Ane6+zXA88AbgUuAY+5+KHw8sRsNFukHTbotSRTnCL4D3AjMbVbA3T/s7n8TvjwL+CeCOVivM7PPmdlRM9PfuJJYq6ur/W6CyI5tG7ruPgdgZttWZmZXABV3f9TMOsBV7v6cmd0DvAm4f0P5I8ARgPHxcaampgAoFAqk02kajQYAw8PDFItFpqenARgaGqJardJoNGi320DwJ/TS0hKtViuqI5VKMTcXfC9ls1kKhUJ0JNatY3Z2lpWVFQAqlQqtViu6c+Do6ChmRrPZBIKx0Pl8PqojlUpRqVSo1+vRjD/VapXFxcWojmKxiLtHV0KOjIyQy+WiURnpdJpyuczMzEwUItVqlYWFBZaWlgAYGxuj0+mwsLAABEP2stkss7OzAGQyGUql0ro6arUazWaT5eVlAEqlEisrK1Ed+XyeTCYTfcbdOqanp3F3zIxarbbuMy6VSrTb7ejWuWfSfpqfn2ffvn3aT3t8P52Jv09biT3ptpk95O6HtlhfBY4Db3b3p80s6+5L4bp3ARl3/8Bm22vSbdnLVldXGRrSmATZe17ySbfNbBj4c+DX3P3pcPG9ZnbQzFLA9cDju/FeIv3QPVITSZIdB7yZvdbMbtuw+G3ApcDN4YiZG4FbgXuBx4BH3P3BF91akT7p/nkvkiSxT3x2u2fc/SvALRvW3QXc1WOzS15M40RE5NunTkWRGMbGxvrdBJEdU8CLxNAd1SGSJAp4kRh0klWSSAEvIjKgFPAiMeh+8JJECniRGLLZbL+bILJjCniRGLqXsYskiQJeRGRAKeBFYshkMv1ugsiOKeBFYiiVSv1ugsiOKeBFYtCEH5JECniRGDThhySRAl5EZEAp4EViqNVq/W6CyI4p4EVi6E4zJ5IkCniRGLpzcYokiQJeRGRAxQp4MzvbzB7epsxRM3vEzG7ZaplIEmkcvCTRtgFvZhXgbqCwRZkbgJS7XwGcb2YHei3brUaLnG4rKyv9boLIjsWZk7UD3Ah8cosyh4CPhc+PA1cC391j2dfXbmRmR4AjAOPj40xNTQFQKBRIp9M0Gg0AhoeHKRaLTE9PAzA0NES1WqXRaNButwEol8ssLS3RarWiOlKpFHNzc0BwN8BCoRBdsNKtY3Z2NvrlrVQqtFotTp48CcDo6ChmFp1gGxkZIZ/PR3WkUikqlQr1ej2a8adarbK4uBjVUSwWcXfm5+ejOnK5HPV6HYB0Ok25XGZmZiYaa12tVllYWIgmeh4bG6PT6USTTuRyObLZbHQDrEwmQ6lUWldHrVaj2WxGfcelUomVlZWojnw+TyaTiT7jbh3T09O4O2ZGrVZb9xmXSiXa7TaLi4tn3H6an59n//792k97fD+dib9PWzF337JAVNDsoe7E2z3WHQU+5O6Pm9k1wKXAgY3L3P2OzeqfmJjwycnJWG0ROd2mpqbYt29fv5sh8gJmdsLdJ3qt262TrPNAd0aE0bDeXstEEimfz/e7CSI7tluhe4KgCwbgIPDUJstEEkl3k5QkitMHv46ZvRZ4i7uvHRlzH/CwmZ0DXAtcDniPZSKJ1Gg01EUjiRP7CL7b/+7uX9kQ7rj7HMGJ1keBw+7e6LVsl9osIiIx7PgIfjPuXufUqJlNl4kkkbpoJIl04lMkBl3oJEmkgBeJoTsWWSRJFPAiMcS9XkRkL1HAi8RgZv1ugsiOKeBFYtCEH5JECniRGLr3BxFJEgW8SAzdmzuJJIkCXkRkQCngRWLQOHhJIgW8SAzqopEkUsCLxNCdlEEkSRTwIiIDSgEvEkOhsOmUxCJ7lgJeJIZ0etduvCpy2ijgRWLQhU6SRLEC3syOmtkjZnbLJut/xsweCh+PmdkfmFnazJ5Zs/zi3W26iIhsZduAN7MbgJS7XwGcb2YHNpZx97vc/VA469PDwB8BlwDHusvd/YldbrvIaTM8PNzvJojsWJwj+EOcmpXpOKcm0n4BMzsXONvdJwnmYL3OzD4X/gWgTkxJrGKx2O8miOxYnNAtAM+Gz2eAS7co+w7grvD554Gr3P05M7sHeBNw/9rCZnYEOAIwPj7O1NRU8IaFAul0Our3HB4eplgsRpMuDA0NUa1WaTQa0QUo5XKZpaUlWq1WVEcqlWJubg6AbDZLoVBgZmZmXR2zs7OsrKwAUKlUaLVanDx5EoDR0VHMjGazCcDIyAj5fD6qI5VKUalUqNfrdDodAKrVKouLi1EdxWIRd2d+fj6qI5fLUa/XgeDkXblcZmZmhtXV1aiOhYUFlpaWABgbG6PT6bCwsABALpcjm80yOzsLBNPJlUqldXXUajWazSbLy8tAcCXmyspKVEc+nyeTyUSfcbeO6elp3B0zo1arrfuMS6US7XY7GhN+Ju2n+fl59u/fr/20x/fTmfj7tBXbbiIDM/sgQVfLo2F3zQXu/ps9yg0Bfw98j7u7mWXdfSlc9y4g4+4f2Ox9JiYmfHJycsu2iPTL1NQU+/bt63czRF7AzE64+0SvdXG6aE5wqlvmIPDUJuVeD3zWT31j3GtmB80sBVwPPB6/ySJ7y9CQBpxJ8sT5X3sf8BNmdifwo8CXzey2HuXeAHxmzetbgXuBx4BH3P3BF9tYkX6pVqv9boLIjm3bB+/uc2Z2CLgaeL+7P0+Po3F3/48bXn+JYCSNSOI1Gg3dUVISJ9bIFnevc2okjcgZR3eTlCRSx6KIyIBSwIvEsN1wNJG9SAEvEkN3DLVIkijgRWLoXvAjkiQKeBGRAaWAF4lBE35IEingRWJIpVL9boLIjingRWLo3mRLJEkU8CIiA0oBLxJDNpvtdxNEdkwBLxKDTrJKEingRWLoTkohkiQKeBGRAaWAF4lBE35IEul/rUgMmvBDkkgBLxJDd0JmkSSJFfBmdtTMHjGzWzZZnzazZ8zsofBxcZztRJJiZWWl300Q2bFtA97MbgBS7n4FcL6ZHehR7BLgmLsfCh9PxNxOREReInGm7DvEqen6jgNXAl/fUOZy4DozOww8Abw9znZmdgQ4AjA+Ps7U1BQQjDlOp9M0Gg0AhoeHKRaLTE9PA8EJr2q1SqPRiKZSK5fLLC0tRbd1LRQKpFKp6BLzbDZLoVCIhrt165idnY2OziqVCq1Wi5MnTwIwOjqKmdFsNgEYGRkhn89HdaRSKSqVCvV6nU6nAwR9tYuLi1EdxWIRd2d+fj6qI5fLUa/Xgx2QTlMul5mZmWF1dTWqY2FhIboH+djYGJ1Oh4WFBQByuRzZbDbqNshkMpRKpXV11Go1ms0my8vLAJRKJVZWVqI68vk8mUwm+oy7dUxPT+PumBm1Wm3dZ1wqlWi32ywuLp5x+8nMALSf9vh+OhN/n7Zi7r51AbOjwIfc/XEzuwa41N3v2FDmMuBb7v6cmd0DfBz4oe22W2tiYsInJye3bItIv8zPzzM6OtrvZoi8gJmdcPeJXuvi9MHPA7nw+egm23zR3Z8Ln08CB2JuJ5II3SNIkSSJE7onCLpXAA4CT/Uoc6+ZHTSzFHA98HjM7URE5CUSpw/+PuBhMzsHuBb4MTO7zd3Xjoy5FfgoYMD97v6gmY1t2O7yXW67yGmj7hlJom0D3t3nzOwQcDXwfnd/nuAIfW2ZLxGMpNlqu8ZuNVrkdOueZBVJkjhH8Lh7nVMjYmL7drcT2WuazaZuGSyJoxOfIiIDSgEvEsPIyEi/myCyYwp4kRjy+Xy/myCyYwp4kRg04YckkQJeRGRAKeBFYkilUv1ugsiOKeBFYqhUKv1ugsiOKeBFYujerVAkSRTwIjF0b18rkiQKeBGRAaWAF4lBk25LEingRWLozrojkiQKeJEYNOGHJJECXkRkQCngRWIoFov9boLIjsUKeDM7amaPmNktm6wvmdkDZnbczD5hZsNmljazZ8zsofBx8e42XeT02W5yepG9aNuAN7MbgJS7XwGcb2YHehR7K3Cnu18DPA+8kWCGp2Pufih8PLGbDRc5nebn5/vdBJEdi3MEf4hTszId59RE2hF3/7C7/0348izgnwjmYL3OzD4X/gUQa/YoERHZHXFCtwA8Gz6fAS7drKCZXQFU3P1RM+sAV7n7c2Z2D/Am4P4N5Y8ARwDGx8eZmpoK3rBQIJ1O02gE07gODw9TLBaZnp4GYGhoiGq1SqPRoN1uA1Aul1laWqLVakV1pFIp5ubmAMhmsxQKhei2r906ZmdnWVlZAYL7jbRarWjExOjoKGZGs9kEgkkf8vl8VEcqlaJSqVCv16MrHavVKouLi1EdxWIRd4+OAEdGRsjlctGl7+l0mnK5zMzMDKurq1EdCwsLLC0tATA2Nkan02FhYQGAXC5HNptldnYWgEwmQ6lUWldHrVaj2WyyvLwMQKlUYmVlJaojn8+TyWSiz7hbx/T0NO6OmVGr1dZ9xqVSiXa7HQ0ZPJP2U/dz1H7a2/vpTPx92opt17doZh8k6Gp5NOyuucDdf7NHuSrBEf6b3f1pM8u6+1K47l1Axt0/sNn7TExM+OTk5JZtEemXTqejO0rKnmRmJ9x9ote6OF00JzjVLXMQeKrHGwwDfw78mrs/HS6+18wOmlkKuB54fKcNF9krdLMxSaI4AX8f8BNmdifwo8CXzey2DWXeRtB1c3M4YuZG4FbgXuAx4BF3f3AX2y0iItvYtg/e3efM7BBwNfB+d3+eDUfj7n4XcFePzS/ZjUaK9Fs6rTECkjyx/te6e51TI2lEzjjbncwS2Yt0JatIDJp0W5JIAS8SQ3e4nEiSKOBFRAaUAl4kBk34IUmkgBeJoXvFokiSKOBFYuhe5i6SJAp4EZEBpYAXiWFsbKzfTRDZMQW8SAzduxuKJIkCXiQGnWSVJFLAi4gMKAW8SAy5XK7fTRDZMQW8SAzZbLbfTRDZMQW8SAzd6dxEkkQBLyIyoBTwIjFkMpl+N0Fkx2IFvJkdNbNHzOyWnZSJs53I6WZmO36Uy+UdbyPSb9vO6GRmNwApd7/CzP7YzA64+9e3KwNcvN12Ii/Wwd84TqPV3tE25/3KX71ErVlv/69+akflS7kMj/+na16i1siZKM6UfYc4NV3fceBKYGNQ9yrz3dttZ2ZHgCMA4+PjTE1NAVAoFEin0zQaDQCGh4cpFotMT08DMDQ0RLVapdFo0G4Hv9zlcpmlpSVarVZURyqVYm5uDghGQRQKhWhmnm4ds7OzrKysAFCpVGi1Wpw8eRKA0dFRzIxmswnAyMgI+Xw+qiOVSlGpVKjX69GVjtVqlcXFxaiOYrGIuzM/Px/VkcvlqNfrQDDXZ7lcZmZmJppUolqtsrCwEN3gamxsjE6nE11sk8vlyGaz0Ym/TCZDqVRaV0etVqPZbLK8vAxAqVRiZWUlqiOfz5PJZKLPuFvH9PQ07o6ZUavV1n3GpVKJdrvN4uLintlPq/t/kSKDYRVot/9hIPcT6PfppdpPWzF337qA2VHgQ+7+uJldA1zq7ndsVwY4sN12a01MTPjk5OSWbRHpl6mpKfbt29fvZoi8gJmdcPeJXuvi9MHPA92rPEY32aZXmTjbiSRCrVbrdxNEdixO6J4g6F4BOAg8FbNMnO1EEqHbrSCSJHH64O8DHjazc4BrgR8zs9vc/ZYtylwOeI9lIonU7XsVSZJtj+DdfY7gJOqjwGF3f3xDuPcq0+i1bHebLiIiW4lzBI+71zk1IiZ2mTjbiSRBqVTqdxNEdkwnPkVi6A79E0kSBbxIDJrwQ5JIAS8iMqC2vYGYJKMAAAN2SURBVNDpdDGzfwae7nc7RDaxD5jqdyNEejjP3c/qtWLPBLzIXmZmk5tdLSiyV6mLRkRkQCngRUQGlAJeJJ4/7HcDRHZKffAiIgNKR/AiIgNKAS9nPDMrmVkqfH6g3+0R2S0KeBlYZnaZmf3Bmud5M7vFzL4/fN29yft7gX8bPv8lM8uH26TX1HWrmR02s9vN7FfNrGhmf939YlhT7pVm9ryZPbTh8ZyZvfI0/NgikVg3GxNJmjB4fxtYNbP3E8xP8BbgWSAF/C7wA2b2GuAngUfN7M3A9wLnhpNmZ83sR4AOMAdcAbwMeDlwHrDg7h0zGwJw91VgaYtmdXb9BxXZggJeBtVPA39HEORDQItglrHLgXOA3wFOAkeBZYLbWkMwOc2jQAZ4wt0bZnYuUAN+DngM+J/h8+80s88A3wlcD3yOIMQ/CnxlQ3suDN9H5LTRKBoZSGH3yscJJn//Y+CvgTsJwhng3wBXA5cBdwBfC5dfBnw+fP5pd7/TzF5O8EXxNeA1BEF9CUHXzj8Cb3f3d5vZTxL8ldAgmEN7oxLBPMUP7OKPKrIp9cHLoEoB7wM+C9zNqW6Wb4WPk+7+v9z9vwF14Jbw8c3w3/cCXzCzCsFfur9BcFR/J0HYPwf8C+BVwP8BcPe7gXcDI8AkQdDPhc/zwHsU7nI6qYtGBtU+gqPpA8BPAX8FfJVgekmAH1lT9mvAReHz3wufG0EoP0XQpXNbWNdB4HXAdwB/EW6zNrSHCIJ/jKC/3oBXh9uuOyEr8lJTwMtAcvdnw5OrLwM+QnBi9HqCcAb4LgAzGwUuBSobqqgAH3T3bwDfMLOPEfTfPwB82d3bZvYPYZ23rtmuSdAlNE3QPTREcIJ3H8HRvMhpo4CXQZYDDhN01XwE+FN3fx+AmT0UlmkB33T3q9ZuaGY/zfoj7o8ACwTdN0+b2XcQfEksEXxBTIblvp9gLuIOp47gxwmCfhJ4Zvd+PJGtKeBlkJWAvwWOAP+yu9DMjhCMioGgG+agmT24YdtzCbprCPvh7yLoa78ceC3wJ8AvAf8P+LiZvSV8v58g6Hvv1m0Eo3cA3mlmz7p79ySuyEtKo2jkjGNmWXdfWvM65+6tbbZJu/tK+NyAIXfvdF+7fpFkD1LAi4gMKA2TFBEZUAp4EZEBpYAXERlQCngRkQGlgBcRGVD/HwqfOB3xsQ73AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(df1),columns=['销售数量'])\n",
    "df.plot.box(title=\"查看异常值\")\n",
    "plt.grid(linestyle=\"--\", alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看重复值\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.groupby(['大类编码','销售金额']).mean() #求得均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td></td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311            生鲜  个   8.0   4.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   6.5   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\cx\\\\文档\\\\实训课\\\\大数据分析综合实训-附件.csv',encoding = 'gbk')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.drop(['销售月份'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42816, 16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号      int64\n",
       "大类编码      int64\n",
       "大类名称     object\n",
       "中类编码      int64\n",
       "中类名称     object\n",
       "小类编码      int64\n",
       "小类名称     object\n",
       "销售日期      int64\n",
       "商品编码     object\n",
       "规格型号     object\n",
       "商品类型     object\n",
       "单位       object\n",
       "销售数量    float64\n",
       "销售金额    float64\n",
       "商品单价    float64\n",
       "是否促销     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'销售日期'] = pd.to_datetime(df.loc[:,'销售日期'].astype(str), format='%Y-%m-%d', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号    0.000000\n",
       "大类编码    0.000000\n",
       "大类名称    0.000000\n",
       "中类编码    0.000000\n",
       "中类名称    0.000000\n",
       "小类编码    0.000000\n",
       "小类名称    0.000000\n",
       "销售日期    0.000047\n",
       "商品编码    0.000000\n",
       "规格型号    0.000000\n",
       "商品类型    0.000000\n",
       "单位      0.000000\n",
       "销售数量    0.000047\n",
       "销售金额    0.000000\n",
       "商品单价    0.000000\n",
       "是否促销    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x : sum(x.isnull())/len(x), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除缺失值前： (42816, 16)\n",
      "删除缺失值后： (42812, 16)\n"
     ]
    }
   ],
   "source": [
    "print('删除缺失值前：', df.shape)\n",
    "data = df.dropna(subset=['销售日期','销售数量'], how='any')\n",
    "print('删除缺失值后：', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号    0.0\n",
       "大类编码    0.0\n",
       "大类名称    0.0\n",
       "中类编码    0.0\n",
       "中类名称    0.0\n",
       "小类编码    0.0\n",
       "小类名称    0.0\n",
       "销售日期    0.0\n",
       "商品编码    0.0\n",
       "规格型号    0.0\n",
       "商品类型    0.0\n",
       "单位      0.0\n",
       "销售数量    0.0\n",
       "销售金额    0.0\n",
       "商品单价    0.0\n",
       "是否促销    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x : sum(x.isnull())/len(x), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>50%</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <td>876.898440</td>\n",
       "      <td>729.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2611.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大类编码</th>\n",
       "      <td>17.934411</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中类编码</th>\n",
       "      <td>1799.465057</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>3436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小类编码</th>\n",
       "      <td>179955.071662</td>\n",
       "      <td>200103.0</td>\n",
       "      <td>100101.0</td>\n",
       "      <td>343699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售数量</th>\n",
       "      <td>1.199212</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售金额</th>\n",
       "      <td>10.609148</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-145.0</td>\n",
       "      <td>5340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>商品单价</th>\n",
       "      <td>12.541680</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>890.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean       50%       min       max\n",
       "顾客编号     876.898440     729.0       0.0    2611.0\n",
       "大类编码      17.934411      20.0      10.0      34.0\n",
       "中类编码    1799.465057    2001.0    1001.0    3436.0\n",
       "小类编码  179955.071662  200103.0  100101.0  343699.0\n",
       "销售数量       1.199212       1.0     -16.0     216.0\n",
       "销售金额      10.609148       5.7    -145.0    5340.0\n",
       "商品单价      12.541680       6.9       0.0     890.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计data信息\n",
    "data.describe().T[['mean', '50%', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>异常值数量</th>\n",
       "      <th>异常值占比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>销售数量</th>\n",
       "      <td>88</td>\n",
       "      <td>0.002055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售金额</th>\n",
       "      <td>85</td>\n",
       "      <td>0.001985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      异常值数量     异常值占比\n",
       "销售数量     88  0.002055\n",
       "销售金额     85  0.001985"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计销售数量异常值的行数和占比\n",
    "lost_count = sum(data[\"销售数量\"]<0)\n",
    "lost_count_prop = lost_count/len(data)\n",
    "# 统计销售金额异常值的行数和占比\n",
    "lost_money = sum(data[\"销售金额\"]<0)\n",
    "lost_money_prop = lost_money/len(data)\n",
    "pd.DataFrame({'异常值数量':[lost_count,lost_money], '异常值占比':[lost_count_prop,lost_money_prop]},index=[\"销售数量\",\"销售金额\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除异常值之前: (42812, 16)\n",
      "删除异常值之后： (42719, 16)\n"
     ]
    }
   ],
   "source": [
    "print('删除异常值之前:',data.shape)\n",
    "data = data[(data.loc[:,'销售数量'] > 0) & (data.loc[:,'销售金额'] > 0)]\n",
    "print('删除异常值之后：',data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>大类名称</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>蔬果</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>粮油</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>日配</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>日配</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>日配</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  大类名称  销售金额\n",
       "0   蔬果   4.0\n",
       "1   粮油   3.0\n",
       "2   日配   2.4\n",
       "3   日配   6.5\n",
       "4   日配  11.9"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取大类名称信息和销售金额\n",
    "data_big = df[['大类名称','销售金额']]\n",
    "data_big.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "蔬果    14192\n",
       "休闲     8849\n",
       "粮油     5874\n",
       "日配     4780\n",
       "洗化     3068\n",
       "酒饮     1903\n",
       "肉禽     1242\n",
       "熟食      889\n",
       "冲调      593\n",
       "家居      591\n",
       "针织      305\n",
       "文体      267\n",
       "水产      195\n",
       "家电       50\n",
       "烘焙       18\n",
       "Name: 大类名称, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 大类名称出现的频次\n",
    "data_big['大类名称'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额总和</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大类名称</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>休闲</th>\n",
       "      <td>74145.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>冲调</th>\n",
       "      <td>13957.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家居</th>\n",
       "      <td>6311.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家电</th>\n",
       "      <td>853.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文体</th>\n",
       "      <td>1970.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日配</th>\n",
       "      <td>81958.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>水产</th>\n",
       "      <td>2891.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洗化</th>\n",
       "      <td>38013.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>烘焙</th>\n",
       "      <td>110.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>熟食</th>\n",
       "      <td>5939.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>粮油</th>\n",
       "      <td>60931.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肉禽</th>\n",
       "      <td>25197.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蔬果</th>\n",
       "      <td>81375.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>酒饮</th>\n",
       "      <td>54790.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>针织</th>\n",
       "      <td>5765.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        销售金额总和\n",
       "大类名称          \n",
       "休闲    74145.20\n",
       "冲调    13957.60\n",
       "家居     6311.10\n",
       "家电      853.90\n",
       "文体     1970.30\n",
       "日配    81958.30\n",
       "水产     2891.00\n",
       "洗化    38013.80\n",
       "烘焙      110.90\n",
       "熟食     5939.94\n",
       "粮油    60931.95\n",
       "肉禽    25197.67\n",
       "蔬果    81375.79\n",
       "酒饮    54790.90\n",
       "针织     5765.90"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据大类名称列分组，求出该值对应的销售金额的总和\n",
    "data_big = data_big.groupby('大类名称').sum()\n",
    "data_big.rename(columns = {'销售金额':'销售金额总和'}, inplace=True)\n",
    "data_big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-27-ea56de240a6e>:4: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=data_big.values,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置图框的大小\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=data_big.values,\n",
    "        labels=data_big.index,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"每个大类商品销售金额统计分析\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td></td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311            生鲜  个   8.0   4.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   6.5   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:\\\\cx\\\\文档\\\\实训课\\\\大数据分析综合实训-附件.csv',encoding = 'gbk')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中类名称</th>\n",
       "      <th>是否促销</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>否</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>酱菜类</td>\n",
       "      <td>否</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>否</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>否</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>否</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   中类名称 是否促销  销售金额\n",
       "0    蔬菜    否   4.0\n",
       "1   酱菜类    否   3.0\n",
       "2  冷藏乳品    否   2.4\n",
       "3  冷藏料理    否   6.5\n",
       "4  冷藏乳品    否  11.9"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取是否促销信息和销售金额\n",
    "data_promotion = data[['中类名称','是否促销','销售金额']]\n",
    "data_promotion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "否      36312\n",
       "是       6502\n",
       "9.9        2\n",
       "Name: 是否促销, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 促销与非促销出现的频次\n",
    "data_promotion['是否促销'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中类名称</th>\n",
       "      <th>是否促销</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>毯子</td>\n",
       "      <td>是</td>\n",
       "      <td>79.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>饼干</td>\n",
       "      <td>是</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>常温乳品</td>\n",
       "      <td>是</td>\n",
       "      <td>33.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>南北干货</td>\n",
       "      <td>是</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>袋装速食面组</td>\n",
       "      <td>是</td>\n",
       "      <td>10.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42797</th>\n",
       "      <td>南北干货</td>\n",
       "      <td>是</td>\n",
       "      <td>33.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42807</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>是</td>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42809</th>\n",
       "      <td>糕点</td>\n",
       "      <td>是</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42810</th>\n",
       "      <td>卫生巾</td>\n",
       "      <td>是</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42813</th>\n",
       "      <td>纸制品</td>\n",
       "      <td>是</td>\n",
       "      <td>12.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6502 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         中类名称 是否促销   销售金额\n",
       "18         毯子    是  79.00\n",
       "19         饼干    是   2.70\n",
       "22       常温乳品    是  33.90\n",
       "27       南北干货    是   8.28\n",
       "34     袋装速食面组    是  10.90\n",
       "...       ...  ...    ...\n",
       "42797    南北干货    是  33.90\n",
       "42807    冷藏乳品    是   9.90\n",
       "42809      糕点    是   5.66\n",
       "42810     卫生巾    是   3.90\n",
       "42813     纸制品    是  12.90\n",
       "\n",
       "[6502 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据是否促销，拆分出销表和非促销表\n",
    "data_promotion_yes = data_promotion.loc[data_promotion['是否促销'] == '是']\n",
    "data_promotion_yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中类名称</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一次性用品</th>\n",
       "      <td>70.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳饮料</th>\n",
       "      <td>673.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五谷杂粮</th>\n",
       "      <td>2935.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>保养用品</th>\n",
       "      <td>77.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>保温容器</th>\n",
       "      <td>117.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>酱菜类</th>\n",
       "      <td>155.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>鞋类护理用品</th>\n",
       "      <td>12.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>食用油</th>\n",
       "      <td>7950.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>饼干</th>\n",
       "      <td>2844.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香/烛</th>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           销售金额\n",
       "中类名称           \n",
       "一次性用品     70.90\n",
       "乳饮料      673.50\n",
       "五谷杂粮    2935.72\n",
       "保养用品      77.30\n",
       "保温容器     117.90\n",
       "...         ...\n",
       "酱菜类      155.40\n",
       "鞋类护理用品    12.90\n",
       "食用油     7950.30\n",
       "饼干      2844.42\n",
       "香/烛        9.90\n",
       "\n",
       "[109 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dy=data_promotion_yes.groupby('中类名称').sum()\n",
    "dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1=dy.sort_values(by='销售金额',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d1['中类名称'].values\n",
    "x=d1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=d1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-d9efd31ad4ad>:6: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=y,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "#画图\n",
    "# 设置图框的大小\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=y,\n",
    "        labels=x,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"中类促销\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td></td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311            生鲜  个   8.0   4.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   6.5   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:\\\\cx\\\\文档\\\\实训课\\\\大数据分析综合实训-附件.csv',encoding = 'gbk')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中类名称</th>\n",
       "      <th>是否促销</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>否</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>酱菜类</td>\n",
       "      <td>否</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>否</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>否</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>否</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   中类名称 是否促销  销售金额\n",
       "0    蔬菜    否   4.0\n",
       "1   酱菜类    否   3.0\n",
       "2  冷藏乳品    否   2.4\n",
       "3  冷藏料理    否   6.5\n",
       "4  冷藏乳品    否  11.9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取是否促销信息和销售金额\n",
    "data_promotion = data[['中类名称','是否促销','销售金额']]\n",
    "data_promotion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中类名称</th>\n",
       "      <th>是否促销</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>否</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>酱菜类</td>\n",
       "      <td>否</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>否</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>否</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>否</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42808</th>\n",
       "      <td>猪肉</td>\n",
       "      <td>否</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42811</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>否</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42812</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>否</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42814</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>否</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42815</th>\n",
       "      <td>进口饮料</td>\n",
       "      <td>否</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36312 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       中类名称 是否促销   销售金额\n",
       "0        蔬菜    否   4.00\n",
       "1       酱菜类    否   3.00\n",
       "2      冷藏乳品    否   2.40\n",
       "3      冷藏料理    否   6.50\n",
       "4      冷藏乳品    否  11.90\n",
       "...     ...  ...    ...\n",
       "42808    猪肉    否   7.67\n",
       "42811    蔬菜    否   3.91\n",
       "42812    蔬菜    否   0.86\n",
       "42814    蔬菜    否   1.84\n",
       "42815  进口饮料    否   8.00\n",
       "\n",
       "[36312 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_promotion_no = data_promotion.loc[data_promotion['是否促销'] == '否']\n",
    "data_promotion_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中类名称</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一次性用品</th>\n",
       "      <td>727.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不锈钢餐具</th>\n",
       "      <td>37.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>个人卫生用品</th>\n",
       "      <td>47.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中式熟菜</th>\n",
       "      <td>477.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳饮料</th>\n",
       "      <td>1972.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香/烛</th>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香烟</th>\n",
       "      <td>15132.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>鸡产品</th>\n",
       "      <td>3296.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>鸭产品</th>\n",
       "      <td>263.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黄酒</th>\n",
       "      <td>531.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            销售金额\n",
       "中类名称            \n",
       "一次性用品     727.30\n",
       "不锈钢餐具      37.00\n",
       "个人卫生用品     47.20\n",
       "中式熟菜      477.45\n",
       "乳饮料      1972.40\n",
       "...          ...\n",
       "香/烛        56.00\n",
       "香烟      15132.00\n",
       "鸡产品      3296.44\n",
       "鸭产品       263.97\n",
       "黄酒        531.60\n",
       "\n",
       "[169 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dn=data_promotion_no.groupby('中类名称').sum()\n",
    "dn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2=dn.sort_values(by='销售金额',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['水果', '蔬菜', '猪肉', '香烟', '常温乳品', '蛋类', '国产白酒', '饼干', '五谷杂粮', '炒货'], dtype='object', name='中类名称')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=d2.index\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[44152.14],\n",
       "       [34335.48],\n",
       "       [19779.  ],\n",
       "       [15132.  ],\n",
       "       [13753.4 ],\n",
       "       [13257.02],\n",
       "       [11699.  ],\n",
       "       [ 9823.53],\n",
       "       [ 9798.35],\n",
       "       [ 6013.68]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=d2.values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-4172af1a7dc5>:5: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=y,\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "# 设置图框的大小\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=y,\n",
    "        labels=x,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"中类非促销\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td></td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311            生鲜  个   8.0   4.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   6.5   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\cx\\\\文档\\\\实训课\\\\大数据分析综合实训-附件.csv',encoding = 'gbk')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对data中销售日期进行时间格式转换，coerce将无效解析设置为NaT\n",
    "df.loc[:,'销售日期'] = pd.to_datetime(df.loc[:,'销售日期'].astype(str), format='%Y-%m-%d', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号             int64\n",
       "大类编码             int64\n",
       "大类名称            object\n",
       "中类编码             int64\n",
       "中类名称            object\n",
       "小类编码             int64\n",
       "小类名称            object\n",
       "销售日期    datetime64[ns]\n",
       "销售月份             int64\n",
       "商品编码            object\n",
       "规格型号            object\n",
       "商品类型            object\n",
       "单位              object\n",
       "销售数量           float64\n",
       "销售金额           float64\n",
       "商品单价           float64\n",
       "是否促销            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号       销售日期  销售金额\n",
       "0     0 2015-01-01   4.0\n",
       "1     1 2015-01-01   3.0\n",
       "2     2 2015-01-01   2.4\n",
       "3     3 2015-01-01   6.5\n",
       "4     4 2015-01-01  11.9"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取顾客编号、销售日期、销售金额\n",
    "data_customer = df[['顾客编号','销售日期','销售金额']]\n",
    "data_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-110-77cf35c2a2ec>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_customer['月份'] = [x.month for x in data_customer['销售日期']]\n",
      "D:\\cx\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py:3990: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>月份</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  销售金额   月份\n",
       "0     0   4.0  1.0\n",
       "1     1   3.0  1.0\n",
       "2     2   2.4  1.0\n",
       "3     3   6.5  1.0\n",
       "4     4  11.9  1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据销售日期列获取消费月份列\n",
    "data_customer['月份'] = [x.month for x in data_customer['销售日期']]\n",
    "data_customer.drop(['销售日期'], axis=1, inplace=True)\n",
    "data_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拆分出四个月的表\n",
    "data_month1 = data_customer.loc[data_customer['月份'] == 1,:]\n",
    "data_month2 = data_customer.loc[data_customer['月份'] == 2,:]\n",
    "data_month3 = data_customer.loc[data_customer['月份'] == 3,:]\n",
    "data_month4 = data_customer.loc[data_customer['月份'] == 4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据顾客编号列分组，求出每位顾客每月的消费额\n",
    "data_month1_cost = data_month1.groupby('顾客编号').sum()\n",
    "data_month1_cost.drop(['月份'], axis=1, inplace=True)\n",
    "data_month2_cost = data_month2.groupby('顾客编号').sum()\n",
    "data_month2_cost.drop(['月份'], axis=1, inplace=True)\n",
    "data_month3_cost = data_month3.groupby('顾客编号').sum()\n",
    "data_month3_cost.drop(['月份'], axis=1, inplace=True)\n",
    "data_month4_cost = data_month4.groupby('顾客编号').sum()\n",
    "data_month4_cost.drop(['月份'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1月消费额</th>\n",
       "      <th>2月消费额</th>\n",
       "      <th>3月消费额</th>\n",
       "      <th>4月消费额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.30</td>\n",
       "      <td>30.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48.70</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.39</td>\n",
       "      <td>360.97</td>\n",
       "      <td>68.87</td>\n",
       "      <td>49.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>154.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1月消费额   2月消费额  3月消费额   4月消费额\n",
       "顾客编号                              \n",
       "0     11.05    0.00   0.00   13.60\n",
       "1     12.30   30.30   0.00    0.00\n",
       "2     48.70   30.00   0.00    0.00\n",
       "3     67.39  360.97  68.87   49.28\n",
       "4     27.90    0.00   0.00  154.94"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将四个消费额表连接，并将NaN替换成0\n",
    "data_month1234_cost = pd.concat([data_month1_cost, data_month2_cost, data_month3_cost, data_month4_cost], axis=1, ignore_index=True)\n",
    "data_month1234_cost.columns = list(['1月消费额', '2月消费额', '3月消费额', '4月消费额'])\n",
    "data_month1234_cost.fillna(0, inplace=True)\n",
    "data_month1234_cost.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据顾客编号列，求出每位顾客每月的消费次数\n",
    "data_month1_times = data_month1['顾客编号'].value_counts(dropna=False)\n",
    "data_month2_times = data_month2['顾客编号'].value_counts(dropna=False)\n",
    "data_month3_times = data_month3['顾客编号'].value_counts(dropna=False)\n",
    "data_month4_times = data_month4['顾客编号'].value_counts(dropna=False)\n",
    "\n",
    "data_month1_times = pd.DataFrame({'顾客编号':data_month1_times.index, '次数':data_month1_times.values})\n",
    "data_month2_times = pd.DataFrame({'顾客编号':data_month2_times.index, '次数':data_month2_times.values})\n",
    "data_month3_times = pd.DataFrame({'顾客编号':data_month3_times.index, '次数':data_month3_times.values})\n",
    "data_month4_times = pd.DataFrame({'顾客编号':data_month4_times.index, '次数':data_month4_times.values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>1月消费次数</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
       "      <th>4月消费次数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>124</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>90</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>86</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  1月消费次数  2月消费次数  3月消费次数  4月消费次数\n",
       "0    51     124    32.0    19.0    38.0\n",
       "1    92      98    28.0     0.0     0.0\n",
       "2    47      90     7.0    63.0    34.0\n",
       "3   108      86    18.0    43.0    29.0\n",
       "4    12      77    30.0    47.0    25.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将四个消费次数表连接\n",
    "data_month12_times = pd.merge(data_month1_times, data_month2_times, how='left', left_on='顾客编号', right_on='顾客编号')\n",
    "data_month34_times = pd.merge(data_month3_times, data_month4_times, how='left', left_on='顾客编号', right_on='顾客编号')\n",
    "data_month1234_times = pd.merge(data_month12_times, data_month34_times, how='left', left_on='顾客编号', right_on='顾客编号')\n",
    "data_month1234_times.columns = list(['顾客编号', '1月消费次数', '2月消费次数', '3月消费次数', '4月消费次数'])\n",
    "data_month1234_times.fillna(0, inplace=True)\n",
    "data_month1234_times.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_month1234_times.set_index('顾客编号',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1月消费次数</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
       "      <th>4月消费次数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>124</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>98</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>90</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>86</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>77</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      1月消费次数  2月消费次数  3月消费次数  4月消费次数\n",
       "顾客编号                                \n",
       "51       124    32.0    19.0    38.0\n",
       "92        98    28.0     0.0     0.0\n",
       "47        90     7.0    63.0    34.0\n",
       "108       86    18.0    43.0    29.0\n",
       "12        77    30.0    47.0    25.0\n",
       "...      ...     ...     ...     ...\n",
       "613        1    11.0     0.0     0.0\n",
       "439        1    17.0     6.0    16.0\n",
       "67         1     0.0     0.0     0.0\n",
       "653        1     0.0     0.0     0.0\n",
       "1171       1     7.0     3.0     0.0\n",
       "\n",
       "[1225 rows x 4 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month1234_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_month1234_times['总和']=data_month1234_times.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1月消费次数</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
       "      <th>4月消费次数</th>\n",
       "      <th>总和</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>124</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>98</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>90</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>86</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>77</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1225 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      1月消费次数  2月消费次数  3月消费次数  4月消费次数     总和\n",
       "顾客编号                                       \n",
       "51       124    32.0    19.0    38.0  213.0\n",
       "92        98    28.0     0.0     0.0  126.0\n",
       "47        90     7.0    63.0    34.0  194.0\n",
       "108       86    18.0    43.0    29.0  176.0\n",
       "12        77    30.0    47.0    25.0  179.0\n",
       "...      ...     ...     ...     ...    ...\n",
       "613        1    11.0     0.0     0.0   12.0\n",
       "439        1    17.0     6.0    16.0   40.0\n",
       "67         1     0.0     0.0     0.0    1.0\n",
       "653        1     0.0     0.0     0.0    1.0\n",
       "1171       1     7.0     3.0     0.0   11.0\n",
       "\n",
       "[1225 rows x 5 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month1234_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=data_month1234_times.sort_values(by='总和',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([52, 210, 51, 47, 12, 108, 395, 372, 231, 32], dtype='int64', name='顾客编号')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=dt.index\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 38., 141.,  51.,  62., 292.],\n",
       "       [ 59.,  72.,  78.,  56., 265.],\n",
       "       [124.,  32.,  19.,  38., 213.],\n",
       "       [ 90.,   7.,  63.,  34., 194.],\n",
       "       [ 77.,  30.,  47.,  25., 179.],\n",
       "       [ 86.,  18.,  43.,  29., 176.],\n",
       "       [ 25.,  39.,  49.,  62., 175.],\n",
       "       [ 32.,  34.,  43.,  63., 172.],\n",
       "       [ 40.,  44.,  58.,  26., 168.],\n",
       "       [ 52.,  44.,  32.,  34., 162.]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=dt.values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
